Rui Tuo

CoRR, 2024

A General Theory for Kernel Packets: from state space model to compactly supported basis.

[BibT_eX]

[DOI]

CoRR, 2024

2023

The Temporal Overfitting Problem with Applications in Wind Power Curve Modeling.

[BibT_eX]

[DOI]

Abhinav Prakash

Yu Ding

Technometrics, January, 2023

Privacy-aware Gaussian Process Regression.

[BibT_eX]

[DOI]

Raktim Bhattacharya

CoRR, 2023

Fitting and Peak Searching of Spectrum for Fourier Transform Infrared Spectrometer.

[BibT_eX]

[DOI]

Proceedings of the 23rd IEEE International Conference on Software Quality, 2023

2022

Gaussian Process-Aided Function Comparison Using Noisy Scattered Data.

[BibT_eX]

[DOI]

Abhinav Prakash

Yu Ding

Technometrics, 2022

Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations.

[BibT_eX]

[DOI]

Haoyuan Chen

J. Mach. Learn. Res., 2022

Uncertainty Quantification for Bayesian Optimization.

[BibT_eX]

[DOI]

Wenjia Wang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Single and Multiple Change-Point Detection with Differential Privacy.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2021

A Sparse Expansion For Deep Gaussian Processes.

[BibT_eX]

[DOI]

Shahin Shahrampour

CoRR, 2021

High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids.

[BibT_eX]

[DOI]

Xiaowei Zhang

CoRR, 2021

2020

On the Improved Rates of Convergence for Matérn-Type Kernel Ridge Regression with Application to Calibration of Computer Models.

[BibT_eX]

[DOI]

Yan Wang

SIAM/ASA J. Uncertain. Quantification, 2020

Kriging Prediction with Isotropic Matern Correlations: Robustness and Experimental Designs.

[BibT_eX]

[DOI]

Wenjia Wang

J. Mach. Learn. Res., 2020

Overcoming the Curse of Dimensionality in Density Estimation with Mixed Sobolev GANs.

[BibT_eX]

[DOI]

Shahin Shahrampour

CoRR, 2020

Projection Pursuit Gaussian Process Regression.

[BibT_eX]

[DOI]

Gecheng Chen

CoRR, 2020

Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features.

[BibT_eX]

[DOI]

Shahin Shahrampour

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Deterministic Sampling of Expensive Posteriors Using Minimum Energy Designs.

[BibT_eX]

[DOI]

Technometrics, 2019

Adjustments to Computer Models via Projected Kernel Calibration.

[BibT_eX]

[DOI]

SIAM/ASA J. Uncertain. Quantification, 2019

2018

Stochastic Convergence of a Nonconforming Finite Element Method for the Thin Plate Spline Smoother for Observational Data.

[BibT_eX]

[DOI]

Zhiming Chen

Wenlong Zhang

SIAM J. Numer. Anal., 2018

Differentially Private Change-Point Detection.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion.

[BibT_eX]

[DOI]

Xu He

Technometrics, 2017

2016

A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties.

[BibT_eX]

[DOI]

SIAM/ASA J. Uncertain. Quantification, 2016

2015

Sequential Exploration of Complex Surfaces Using Minimum Energy Designs.

[BibT_eX]

[DOI]

Technometrics, 2015

2014

Surrogate Modeling of Computer Experiments With Different Mesh Densities.

[BibT_eX]

[DOI]

Dan Yu

Technometrics, 2014

Building Accurate Emulators for Stochastic Simulations via Quantile Kriging.

[BibT_eX]

[DOI]

Matthew Plumlee

Technometrics, 2014

2013

Comment: A Brownian Motion Model for Stochastic Simulation With Tunable Precision.

[BibT_eX]

[DOI]

Peter Z. G. Qian